def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
# for instance for the test case_shakemap the haz_sitecol
# has sids in range(0, 26) while sitecol.sids is
# [8, 9, 10, 11, 13, 15, 16, 17, 18];
# the total assetcol has 26 assets on the total sites
# and the reduced assetcol has 9 assets on the reduced sites
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs, imts)
return sitecol, assetcol
def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
extra = self.riskmodel.get_extra_imts(oq.risk_imtls)
if extra:
logging.warn('There are risk functions for not available IMTs '
'which will be ignored: %s' % extra)
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol, oq.asset_hazard_distance
or oq.region_grid_spacing)
assetcol = assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
gmfs = to_gmfs(shakemap, oq.cross_correlation, oq.site_effects,
oq.truncation_level, E, oq.random_seed, oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs)
events = numpy.zeros(E, readinput.stored_event_dt)
events['eid'] = numpy.arange(E, dtype=U64)
self.datastore['events'] = events
return sitecol, assetcol
def read_shakemap(self):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
haz_sitecol = self.datastore.parent['sitecol']
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
self.sitecol, shakemap = get_sitecol_shakemap(
smap, haz_sitecol, oq.asset_hazard_distance
or oq.region_grid_spacing)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
gmfs = to_gmfs(shakemap, oq.site_effects, oq.truncation_level, E,
oq.random_seed)
save_gmf_data(self.datastore, self.sitecol, gmfs)
events = numpy.zeros(E, readinput.stored_event_dt)
events['eid'] = numpy.arange(E, dtype=U64)
self.datastore['events'] = events
def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
extra = self.riskmodel.get_extra_imts(oq.risk_imtls)
if extra:
logging.warning('There are risk functions for not available IMTs '
'which will be ignored: %s' % extra)
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol = assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs, imts)
return sitecol, assetcol
def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
# for instance for the test case_shakemap the haz_sitecol
# has sids in range(0, 26) while sitecol.sids is
# [8, 9, 10, 11, 13, 15, 16, 17, 18];
# the total assetcol has 26 assets on the total sites
# and the reduced assetcol has 9 assets on the reduced sites
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'],
oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(
shakemap, oq.spatial_correlation, oq.cross_correlation,
oq.site_effects, oq.truncation_level, E, oq.random_seed,
oq.imtls)
save_gmf_data(self.datastore, sitecol, gmfs, imts)
return sitecol, assetcol
def test_gmfs(self):
f1 = os.path.join(CDIR, 'ghorka_grid.xml')
f2 = os.path.join(CDIR, 'ghorka_uncertainty.xml')
array = get_shakemap_array(f1, f2)
sitecol, shakemap = get_sitecol_shakemap(array, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
gmf_by_imt = mean_gmf(shakemap)
aae(gmf_by_imt, [0.0047045, 0.0184625, 0.0346171, 0.0175625])
def test_gmfs(self):
f1 = os.path.join(CDIR, 'ghorka_grid.xml')
f2 = os.path.join(CDIR, 'ghorka_uncertainty.xml')
array = get_shakemap_array(f1, f2)
sitecol, shakemap = get_sitecol_shakemap(array, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
gmf_by_imt = mean_gmf(shakemap)
aae(gmf_by_imt, [0.0058806, 0.0230781, 0.0432714, 0.0219532])
def test_gmfs(self):
f1 = os.path.join(CDIR, 'ghorka_grid.xml')
f2 = os.path.join(CDIR, 'ghorka_uncertainty.xml')
array = get_shakemap_array(f1, f2)
sitecol, shakemap = get_sitecol_shakemap(array, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
gmf_by_imt = mean_gmf(shakemap)
aae(gmf_by_imt, [0.0058806, 0.0230781, 0.0432714, 0.0219532])
def test_gmfs(self):
f1 = os.path.join(CDIR, 'ghorka_grid.xml')
f2 = os.path.join(CDIR, 'ghorka_uncertainty.xml')
array = get_shakemap_array(f1, f2)
sitecol, shakemap = get_sitecol_shakemap(array, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
gmf_by_imt, _ = mean_std(shakemap, site_effects=True)
aae(gmf_by_imt, [[0.005391, 0.0223217, 0.0399937, 0.0183143],
[0.0061, 0.025619, 0.0487997, 0.0225788],
[0.0060717, 0.0253156, 0.0478506, 0.0219296],
[0.007087, 0.0298716, 0.0622145, 0.0290721]])
def test_gmfs(self):
f1 = os.path.join(CDIR, 'ghorka_grid.xml')
f2 = os.path.join(CDIR, 'ghorka_uncertainty.xml')
array = get_shakemap_array(f1, f2)
sitecol, shakemap = get_sitecol_shakemap(array, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
gmf_by_imt, _ = mean_std(shakemap, site_effects=True)
aae(gmf_by_imt, [[0.0062040, 0.0262588, 0.0497097, 0.0239060],
[0.0069831, 0.0298023, 0.0602146, 0.0294691],
[0.0069507, 0.0296108, 0.0594237, 0.0286251],
[0.0080306, 0.0341960, 0.0762080, 0.0379360]])
def test_from_files(self):
# files provided by Vitor Silva, without site amplification
f1 = os.path.join(CDIR, 'test_shaking.xml')
f2 = os.path.join(CDIR, 'test_uncertainty.xml')
array = get_shakemap_array(f1, f2)
sitecol, shakemap = get_sitecol_shakemap(array, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
gmf_by_imt, std_by_imt = mean_std(shakemap, site_effects=False)
# PGA, SA(0.3), SA(1.0), SA(3.0)
aae(gmf_by_imt, [[0.0975815, 0.2442196, 0.0286512, 0.6358019],
[0.2023841, 0.5013746, 0.0297236, 0.6544367],
[0.3010831, 0.5986038, 0.0305651, 0.6575208],
[0.3868380, 0.9331248, 0.0296789, 0.6393688]])
aae(std_by_imt, [[0.5922380, 0.6723980, 0.6325073, 0.6445988],
[0.6077153, 0.6661571, 0.6296381, 0.668559],
[0.6146356, 0.6748830, 0.6714424, 0.6613612],
[0.5815353, 0.6460007, 0.6491335, 0.6603457]])
def test_from_files(self):
# files provided by Vitor Silva, without site amplification
f1 = os.path.join(CDIR, 'test_shaking.xml')
f2 = os.path.join(CDIR, 'test_uncertainty.xml')
array = get_shakemap_array(f1, f2)
sitecol, shakemap = get_sitecol_shakemap(array, imt_dt.names)
n = 4 # number of sites
self.assertEqual(len(sitecol), n)
gmf_by_imt, std_by_imt = mean_std(shakemap, site_effects=False)
# PGA, SA(0.3), SA(1.0), SA(3.0)
aae(gmf_by_imt, [[0.1168263, 0.3056736, 0.0356231, 0.7957914],
[0.2422977, 0.6275377, 0.0369565, 0.8191154],
[0.3604619, 0.7492331, 0.0380028, 0.8229756],
[0.4631292, 1.1679310, 0.0369009, 0.8002559]])
aae(std_by_imt, [[0.5922380, 0.6723980, 0.6325073, 0.6445988],
[0.6077153, 0.6661571, 0.6296381, 0.668559],
[0.6146356, 0.6748830, 0.6714424, 0.6613612],
[0.5815353, 0.6460007, 0.6491335, 0.6603457]])
def read_shakemap(self, haz_sitecol, assetcol):
"""
Enabled only if there is a shakemap_id parameter in the job.ini.
Download, unzip, parse USGS shakemap files and build a corresponding
set of GMFs which are then filtered with the hazard site collection
and stored in the datastore.
"""
oq = self.oqparam
E = oq.number_of_ground_motion_fields
oq.risk_imtls = oq.imtls or self.datastore.parent['oqparam'].imtls
logging.info('Getting/reducing shakemap')
with self.monitor('getting/reducing shakemap'):
# for instance for the test case_shakemap the haz_sitecol
# has sids in range(0, 26) while sitecol.sids is
# [8, 9, 10, 11, 13, 15, 16, 17, 18];
# the total assetcol has 26 assets on the total sites
# and the reduced assetcol has 9 assets on the reduced sites
smap = oq.shakemap_id if oq.shakemap_id else numpy.load(
oq.inputs['shakemap'])
sitecol, shakemap, discarded = get_sitecol_shakemap(
smap, oq.imtls, haz_sitecol,
oq.asset_hazard_distance['default'], oq.discard_assets)
if len(discarded):
self.datastore['discarded'] = discarded
assetcol.reduce_also(sitecol)
logging.info('Building GMFs')
with self.monitor('building/saving GMFs'):
imts, gmfs = to_gmfs(shakemap, oq.spatial_correlation,
oq.cross_correlation, oq.site_effects,
oq.truncation_level, E, oq.random_seed,
oq.imtls)
N, E, M = gmfs.shape
events = numpy.zeros(E, rupture.events_dt)
events['id'] = numpy.arange(E, dtype=U32)
self.datastore['events'] = events
# convert into an array of dtype gmv_data_dt
lst = [(sitecol.sids[s], ei) + tuple(gmfs[s, ei])
for s in numpy.arange(N, dtype=U32)
for ei, event in enumerate(events)]
oq.hazard_imtls = {imt: [0] for imt in imts}
data = numpy.array(lst, oq.gmf_data_dt())
create_gmf_data(self.datastore, len(imts), data=data)
return sitecol, assetcol
